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Equipment Failure prediction

What is Predictive Maintenance?

Predictive maintenance is a data-driven approach that uses predictive modeling to assess the state of equipment and determine the optimal timing for maintenance activities. This technique is particularly beneficial in industries that heavily rely on equipment for their operations, such as manufacturing, transportation, energy, and healthcare. Depending on the requirements and challenges of an organization, predictive maintenance may contribute to one or several of the following goals:

Improve equipment reliability:

By proactively identifying and addressing potential problems with equipment, predictive maintenance can help improve the reliability of the equipment, reducing the risk of unexpected downtime or failure.
Increase efficiency: Predictive maintenance can help improve the efficiency of equipment by identifying and fixing problems before they cause equipment failure or downtime. This can help reduce maintenance costs and increase productivity.
Improve safety: Predictive maintenance can help improve safety by identifying and addressing potential problems with equipment before they occur. This can help prevent accidents and injuries caused by equipment failure.
Reduce maintenance costs: By proactively identifying and fixing potential problems with equipment, predictive maintenance can help reduce the overall cost of maintenance by minimizing the need for unscheduled downtime.
Improve asset management: Predictive maintenance can help improve asset management by providing data and insights into the condition and performance of equipment. This can help organizations decide when to replace or upgrade equipment.
Next, we look at the different ways organizations can implement predictive maintenance.

Utilities and manufacturing are only two of the many industries that use predictive maintenance. Image generated with Midjourney.
Approaches to Predictive Maintenance
There are several approaches to implementing a predictive maintenance solution, depending on the type of equipment being monitored and the resources available. These approaches include:

Condition-based monitoring:

This involves continuously monitoring the condition of the equipment using sensors. When certain thresholds or conditions are met, an alert is triggered, or corrective measures are launched. The goal is to reduce the risk of failure. For example, if the temperature of a motor exceeds a certain level, this may indicate that the motor is about to fail.
Predictive modeling: This approach involves using machine learning algorithms to analyze historical lifetime data about the equipment to identify patterns that may indicate an impending failure. This can be done using data from sensors, as well as operational data and maintenance records. When historical or failure data is not available, a degradation model can be created to estimate failure times based on a threshold value. This approach is often used when there is limited data available.
Prognostic algorithms: By using data from sensors and other sources, prognostic algorithms can predict the remaining useful life of a piece of equipment. This information can help organizations determine the likelihood of a breakdown and plan for replacements or maintenance activities. By understanding the equipment better, organizations can potentially extend maintenance cycles, which can reduce costs for replacements and maintenance.
It is important to choose an approach that is appropriate for the specific equipment and maintenance challenges faced by the organization.

Data Requirements:

When implementing predictive maintenance, it is important to consider that each approach comes with its own set of data requirements. Types of data include the following:

Current condition data includes information about the state of the equipment, such as its temperature, pressure, vibration, and other physical parameters.
Operating data includes information about how the equipment is being used, such as its load, speed, and other operating parameters.
Maintenance history data includes information about past maintenance activities that have been performed on the equipment.
Failure history data includes information about past equipment failures, such as the date of the failure, the cause of the failure, and the impact on operations.
Collecting these data requires investing in sensors and other data collection infrastructure and ensuring that data collection is accurate and storage is proper. By combining various data types, organizations can create a comprehensive view of equipment condition and performance and use it to predict maintenance requirements.

The specific types of data needed will depend on the implementation approach. Organizations must ensure they have access to the necessary data to implement the selected approach effectively. Some specific data requirements for each approach include the following:

Approach Data Requirements
Condition-based monitoring Sensor data from the equipment being monitored.
Predictive modeling A combination of sensor data, operational data, and maintenance records.
Prognostic algorithms Sensor data, as well as data about past failures and maintenance events.
Data requirements per implementation approach